Statistical correlation analysis and data frequency
In time series analysis of a given set of variables，practitioners often have to decide whether to use monthly，quarterly,or annual data.They usually try to use the time series data of the higher frequency in order to increase the number of observations. However，time series data of different frequencies and different time spans are often available to empirical studies. They are usually changed to a common time interval through temporal aggregation of systempling，depending on whether the variables are flow variables or stock variables respectively. Several papers have documented the fact that time aggregation potentially distorts the relationship between variables. The objective of this paper is to investigate the impact on the correlation of four kinds of variables which are additive,multiplicative，systematicdally sampled and temporal aggregated. We show that the n-period correlation between variables decreases monotonically or approaches to one-period correlation as n increases. These results in this paper can be applied in finance，economics，and other fields where correlation or regression analyses are employed.
时间序列 集合分析 回归分析 时间间隔
Rong Jea Ching-Fen Fuh
Department of Applied Mathematics,Chinese Culture University,Taipei,Taiwan Department of Applied Mathematics,Chinese Culture University, Taipei, Taiwan
国内会议
北京
英文
69-78
2008-11-11（万方平台首次上网日期，不代表论文的发表时间）